The computerized estimation and predictive analysis of healthcare related data for subjects is becoming increasingly more valuable since it means that appropriate healthcare can be provided to subjects more efficiently and effectively. This form of predictive analytics can be used to build prognostic models from data (for example, using statistical or machine learning methods) to estimate the probability (i.e. likelihood or risk) of the occurrence of a medical event and/or to estimate any future benefit or success to be achieved by circumventing the occurrence of that medical event. There are many techniques based around prognostic models for public and clinical health including preventive intervention, diagnostics, treatment planning, outcome prediction, clinical deterioration detection, and so on.
In the domain of clinical deterioration detection, a prognostic model estimates a short-term risk of an upcoming worsening episode of a chronically ill patient who is remotely managed by telehealth. A timely and reliable estimate allows for an early intervention before a hospitalization is warranted. Such a prognostic model is constructed from the vital sign measurements (such as body weight, heart rate, blood pressure and SpO2 for heart failure patient) and self-reported signs and symptoms (such as fatigue, shortness of breath and oedema for a heart failure patient) with an unplanned hospitalization event as a predicted outcome. These prognostic models are based on a simple analysis of data to determine any change in the condition of a subject such as whether the condition of the subject has worsened, improved or remained stable. At discharge of a subject admitted for an acute event, prognostic models can be used in deciding on the best discharge treatment options (such as the use of telehealth or community services). Prognostic models can also be useful in patient monitoring. For example, prognostic models can assist in clinical decision making and alert clinical staff on patient deterioration, and can be useful for the patient, his peers and healthcare professionals in deciding on a care plan to be followed, an immediate intervention, decision making, lifestyle changes or treatment and/or patient education.
In some existing models, a risk score can be determined for a subject over time and the risk score can be used to provide an insight into the health of a subject over time. However, the correct interpretation of a subject risk score is subtle as there are different meanings of “risks”, and such interpretation may be critical for adequately assessing a subject health condition(s), thereby determining the appropriate course of action for this subject. There is a risk defined at population or cohort level (sometimes called an epidemiological risk or group risk), which entails estimating a mean risk from a group of subjects. There is also a risk coupled to a single individual (sometimes called the clinical risk or individual risk), which reflects an outcome estimation for a target subject with a known set of values for the variables in a prognostic model.
Additionally, in some existing models, risk scores are essentially used as probabilities and thus only make sense for a group of subjects. For example, if a risk score expresses that the chance of hospitalization is 20% for a group of 100 subjects, this means that 20 subjects in that group on average will experience a hospitalization. This makes it difficult to relate an outcome of a risk score analysis to a particular individual subject as an individual risk is only equal to a group risk if all members of the group are equally likely to experience a hospitalization. In addition, risk scores are only estimates and thus are not always accurate. This means that risk scores can be unreliable and may result in false alarms.
At the same time, current techniques for analyzing risk scores are slow. For example, the lack of a link between a group estimated risk score and an individual subject often means that further analysis of data acquired from the subject is required by healthcare professionals before a decision can be taken on the healthcare to provide to the subject. However, any delay in analyzing risk scores can prove critical since time is often short and limited in the healthcare field. Thus, in the existing models that use risk scores, serious events can be missed.
Therefore, there is a need for a method (e.g. a computer implemented method) and apparatus that can determine a likelihood of occurrence of a medical event for a subject in a more robust, reliable and efficient manner.